Are census data accurate for estimating coverage of a lymphatic filariasis MDA campaign? Results of a survey in Sierra Leone


Autoři: Wogba Kamara aff001;  Kathryn L. Zoerhoff aff002;  Emily H. Toubali aff003;  Mary H. Hodges aff004;  Donal Bisanzio aff002;  Dhuly Chowdhury aff005;  Mustapha Sonnie aff004;  Edward Magbity aff006;  Mohamed Samai aff007;  Abdulai Conteh aff008;  Florence Macarthy aff008;  Margaret Baker aff002;  Joseph B. Koroma aff009
Působiště autorů: Statistics Sierra Leone, Circular Road, Tower Hill, Freetown, Sierra Leone aff001;  RTI International, Washington, DC, United States of America aff002;  Helen Keller International, New York, NY, United States of America aff003;  Helen Keller International, Freetown, Sierra Leone aff004;  RTI International, Rockville, MD, United States of America aff005;  Ministry of Health and Sanitation, Freetown, Sierra Leone aff006;  University of Sierra Leone, Freetown, Sierra Leone aff007;  Neglected Tropical Disease Control Program, New England, Freetown, Sierra Leone aff008;  FHI360, Accra, Ghana aff009
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224422

Souhrn

Background

Preventive chemotherapy was administered to 3.2 million Sierra Leoneans in 13 health districts for lymphatic filariasis, onchocerciasis, and soil transmitted helminthes from October 2008 to February 2009. This paper aims to report the findings of a coverage survey conducted in 2009, compare the coverage survey findings with two reported rates for lymphatic filariasis coverage obtained using pre-mass drug administration (MDA) registration and national census projections, and use the comparison to understand the best source of population estimates in calculating coverage for NTD programming in Sierra Leone.

Methodology/Principal findings

Community drug distributors (CDDs) conducted a pre- MDA registration of the population. Two coverage rates for MDA for lymphatic filariasis were subsequently calculated using the reported number treated divided by the total population from: 1) the pre-MDA register and 2) national census projections. A survey was conducted to validate reported coverage data. 11,602 persons participated (response rate of 76.8%). Overall, reported coverage data aggregated to the national level were not significantly different from surveyed coverage (z-test >0.05). However, estimates based on pre-MDA registration have higher agreement with surveyed coverage (mean Kendall’s W = 0.68) than coverage calculated with census data (mean Kendall’s = 0.59), especially in districts with known large-scale migration, except in a highly urban district where it was more challenging to conduct a pre-MDA registration appropriately. There was no significant difference between coverage among males versus females when the analyses were performed excluding those women who were pregnant at the time of MDA. The surveyed coverage estimate was near or below the minimum 65% epidemiological coverage target for lymphatic filariasis MDA in all districts.

Conclusion/Significance

These results from Sierra Leone illustrate the importance of choosing the right denominator for calculating treatment coverage for NTD programs. While routinely reported coverage results using national census data are often good enough for programmatic decision making, census projections can quickly become outdated where there is substantial migration, e.g. due to the impact of civil war, with changing economic opportunities, in urban settings, and where there are large migratory populations. In districts where this is known to be the case, well implemented pre-MDA registration can provide better population estimates. Pre-MDA registration should, however, be implemented correctly to reduce the risk of missing pockets of the population, especially in urban settings.

Klíčová slova:

Age groups – Census – Drug administration – Onchocerciasis – Pregnancy – Sierra Leone – Surveys – Lymphatic filariasis


Zdroje

1. Hotez PJ, Molyneux DH, Fenwick A, Kumaresan J, Sachs SE, Sachs JD, et al. Control of neglected tropical diseases. N Engl J Med. 2007;357(10):1018–27. doi: 10.1056/NEJMra064142 17804846.

2. Sachs JD, Hotez PJ. Fighting tropical diseases. Science. 2006;311(5767):1521. Epub 2006/03/18. doi: 10.1126/science.1126851 16543418.

3. Conteh L, Engels T, Molyneux D. Socioeconomic aspects of neglected tropical diseases. The Lancet. 2010;375(9710):239–47.

4. Koroma D, Turay A, Moigua M. Republic of Sierra Leone: 2004 population and housing census: Analytical report on population projection for Sierra Leone. Freetown, Sierra Leone 2006.

5. United Nations. Human Development Report 2010 The Real Wealth of Nations: Pathways to Human Development. New York, NY: 2010.

6. Ottesen EA, Hooper PJ, Bradley M, Biswas G. The global programme to eliminate lymphatic filariasis: health impact after 8 years. PLoS Negl Trop Dis. 2008;2(10):e317. Epub 2008/10/09. doi: 10.1371/journal.pntd.0000317 18841205; PubMed Central PMCID: PMCPMC2556399.

7. World Health Organization. Preventive chemotherapy in human helminthiasis. Geneva, Switzerland;2006.

8. Van den Enden E. Pharmacotherapy of helminth infection. Expert Opinion on Pharmacotherapy. 2009;10(3):435–51. doi: 10.1517/14656560902722463 19191680

9. World Health Organization. Monitoring drug coverage for preventive chemotherapy. Geneva, Switzerland;2010.

10. World Health Organization. Global Programme to Eliminate Lymphatic Filariasis: Monitoring and Epidemiological Assessment of Mass Drug Administration. Geneva, Switzerland;2011.

11. African Programme for Onchocerciasis Control. Community-Directed Treatment with Ivermectin—A Practical Guide for Trainers of Community Drug Distributors. World Health Organization,; 1998.

12. Dietz V, Venczel L, Izurieta H, Stroh G, Zell E, Monterroso E. Assessing and monitoring vaccination coverage levels: lessons from the Americas. Rev Panam Salud Publica/Pan Am J Public Health. 2004;16(6):432–42.

13. Brown D, Burton A, Dobo M, Mihigo R. Proportionate target population estimates used by national immunization programmes in Sub-Saharan Africa and comparison with values from an external source. World Journal of Vaccines. 2014;4:147–56. doi: 10.4236/wjv.2014.43017

14. Zuber P, Yameogo K, Yameogo A, Otten M Jr. Use of administrative data to estimate mass vaccination campaign coverage. Journal of Infectious Diseases. 2003;187(Supplement 1):86–90. doi: 10.1086/368052 12721897

15. Lim SS, Stein DB, Charrow A, Murray CJ. Tracking progress towards universal childhood immunisation and the impact of global initiatives: a systematic analysis of three-dose diphtheria, tetanus, and pertussis immunisation coverage. Lancet. 2008;372(9655):2031–46. Epub 2008/12/17. doi: 10.1016/S0140-6736(08)61869-3 19070738.

16. Huhn G, Brown J, Perea W, Berthe A, Otero H, Libeau G, et al. Vaccination coverage survey versus administrative data in the assessment of mass yellow fever immunization in internally displaced persons—Liberia, 2004. Vaccine. 2006;24(2006):730–37.

17. Kaiser R, Chakauya J, Shibeshi M. Trends in differences between births and surviving infants reported for immunization program planning and external data sources in Eastern and Southern Africa 2000–2013. Vaccine. 2016;34(9):1148–51. doi: 10.1016/j.vaccine.2015.05.074 26057134

18. Statistics Sierra Leone (SSL) and ICF Macro. Sierra Leone Demographic and Health Survey 2008. Calverton, Maryland, USA: MEASURE DHS, 2009.

19. Adams AM, Vuckovic M, Birch E, Brant TA, Bialek S, Yoon D, et al. Eliminating neglected tropical diseases in urban areas: A review of challenges, strategies and research directions for successful mass drug administration. Trop Med Infect Dis. 2018;3(4). Epub 2018/11/25. doi: 10.3390/tropicalmed3040122 30469342; PubMed Central PMCID: PMCPMC6306919.

20. McGinn T. Instructions for probability proportional to size sampling technique. Columbia University; 2004.

21. R Core Team. R: A language for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.

22. Brezger A, Kneib T, S. L. BayesX: Analyzing Bayesian Structured Additive Regression Models. 14. 2005;11:1–22.

23. Fahrmeir L, Kneib T, S. L. Penalized structured additive regression for space-time data: a Bayesian perspective. Statistica Sinica. 2004:731–61.

24. Bretz F, Hothorn T, Westfall P. Multiple comparisons using R. Boca Raton: CRC Press; 2010.

25. Newcombe RG. Interval estimation for the difference between independent proportions: comparison of eleven methods. Stat Med. 1998;17(8):873–90. Epub 1998/05/22. doi: 10.1002/(sici)1097-0258(19980430)17:8<873::aid-sim779>3.0.co;2-i 9595617.

26. Stolk W, Swaminathan S, van Oortmarssen G, Das P, Habbema J. Prospects for elimination of bancroftian filariasis by mass drug treatment in Pondicherry, India: a simulation study. Journal of Infectious Diseases. 2003;188(9):1371–81. doi: 10.1086/378354 14593597

27. Murthy BN, Radhakrishna S, Venkatasubramanian S, Periannan V, Lakshmi A, Joshua V, et al. Lot quality assurance sampling for monitoring immunization coverage in Madras City. Indian Pediatr. 1999;36(6):555–9. 10736582.

28. Biedron C, Pagano M, Hedt BL, Kilian A, Ratcliffe A, Mabunda S, et al. An assessment of lot quality assurance sampling to evaluate malaria outcome indicators: extending malaria indicator surveys. Int J Epidemiol. 2010;39(1):72–9. Epub 2010/02/09. doi: 10.1093/ije/dyp363 20139435; PubMed Central PMCID: PMCPMC2912491.

29. Izurieta H, Venczel L, Dietz V, Tambini G, Barrezueta O, Carrasco P, et al. Monitoring measles eradication in the region of the Americas: critical activities and tools. J Infect Dis. 2003;187 Suppl 1:S133–9. Epub 2003/05/02. doi: 10.1086/368028 12721904.

30. Greenland K, Rondy M, Chevez A, Sadozai N, Gasasira A, Abanida E, et al. Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage. Tropical Medicine and International Health. 2011;16(7):863–8. doi: 10.1111/j.1365-3156.2011.02770.x 21481106

31. Chesnaye N, Sinuon M, Socheat D, Koporc K, Mathieu E. Treatment coverage survey after a school-based mass distribution of mebendazole: Kampot Province, Cambodia. Acta Trop. 2011;118(1):21–6. Epub 2011/01/18. doi: 10.1016/j.actatropica.2010.12.013 21238424.

32. Worrell C, Mathieu E. Drug coverage surveys for neglected tropical diseases: 10 years of field experience. Am J Trop Med Hyg. 2012;87(2):216–22. Epub 2012/08/03. doi: 10.4269/ajtmh.2012.12-0167 22855750; PubMed Central PMCID: PMCPMC3414555.

33. Mathieu E, Deming M, Lammie PJ, McLaughlin SI, Beach MJ, Deodat DJ, et al. Comparison of methods for estimating drug coverage for filariasis elimination, Leogane Commune, Haiti. Trans R Soc Trop Med Hyg. 2003;97(5):501–5. Epub 2004/08/17. doi: 10.1016/s0035-9203(03)80006-8 15307410.

34. Richards FO, Eigege A, Miri ES, Kal A, Umaru J, Pam D, et al. Epidemiological and entomological evaluations after six years or more of mass drug administration for lymphatic filariasis elimination in Nigeria. PLoS Negl Trop Dis. 2011;5(10):e1346. Epub 2011/10/25. doi: 10.1371/journal.pntd.0001346 22022627; PubMed Central PMCID: PMCPMC3191131.

35. Babu BV, Mishra S. Mass drug administration under the programme to eliminate lymphatic filariasis in Orissa, India: a mixed-methods study to identify factors associated with compliance and non-compliance. Trans R Soc Trop Med Hyg. 2008;102(12):1207–13. Epub 2008/07/18. doi: 10.1016/j.trstmh.2008.05.023 18632125.

36. McLaughlin SI, Radday J, Michel MC, Addiss DG, Beach MJ, Lammie PJ, et al. Frequency, severity, and costs of adverse reactions following mass treatment for lymphatic filariasis using diethylcarbamazine and albendazole in Leogane, Haiti, 2000. Am J Trop Med Hyg. 2003;68(5):568–73. Epub 2003/06/19. doi: 10.4269/ajtmh.2003.68.568 12812348.

37. Worldpop: High resolution age-structured populatin distribution maps [Internet]. GeoData Institute, University of Southampton. [cited 12 January 2016]. Available from: www.worldpop.org.

38. https://landscan.ornl.gov/.


Článek vyšel v časopise

PLOS One


2019 Číslo 12